@article{MarionMcInernyPageletal.2012,
  author    = {Marion, Glenn and McInerny, Greg J. and Pagel, J{\"o}rn and Catterall, Stephen and Cook, Alex R. and Hartig, Florian and O\&rsquo, and Hara, Robert B.},
  title     = {Parameter and uncertainty estimation for process-oriented population and distribution models: data, statistics and the niche},
  series = {JOURNAL OF BIOGEOGRAPHY},
  volume    = {39},
  journal   = {JOURNAL OF BIOGEOGRAPHY},
  number    = {12},
  publisher = {WILEY-BLACKWELL},
  address   = {HOBOKEN},
  issn      = {0305-0270},
  doi       = {10.1111/j.1365-2699.2012.02772.x},
  pages     = {2225 -- 2239},
  year      = {2012},
  abstract  = {The spatial distribution of a species is determined by dynamic processes such as reproduction, mortality and dispersal. Conventional static species distribution models (SDMs) do not incorporate these processes explicitly. This limits their applicability, particularly for non-equilibrium situations such as invasions or climate change. In this paper we show how dynamic SDMs can be formulated and fitted to data within a Bayesian framework. Our focus is on discrete state-space Markov process models which provide a flexible framework to account for stochasticity in key demographic processes, including dispersal, growth and competition. We show how to construct likelihood functions for such models (both discrete and continuous time versions) and how these can be combined with suitable observation models to conduct Bayesian parameter inference using computational techniques such as Markov chain Monte Carlo. We illustrate the current state-of-the-art with three contrasting examples using both simulated and empirical data. The use of simulated data allows the robustness of the methods to be tested with respect to deficiencies in both data and model. These examples show how mechanistic understanding of the processes that determine distribution and abundance can be combined with different sources of information at a range of spatial and temporal scales. Application of such techniques will enable more reliable inference and projections, e.g. under future climate change scenarios than is possible with purely correlative approaches. Conversely, confronting such process-oriented niche models with abundance and distribution data will test current understanding and may ultimately feedback to improve underlying ecological theory.},
  language  = {en}
}
@article{RothkegelTrukenbrodSchuettetal.2017,
  author    = {Rothkegel, Lars Oliver Martin and Trukenbrod, Hans Arne and Sch{\"u}tt, Heiko Herbert and Wichmann, Felix A. and Engbert, Ralf},
  title     = {Temporal evolution of the central fixation bias in scene viewing},
  series = {Journal of vision},
  volume    = {17},
  journal   = {Journal of vision},
  publisher = {Association for Research in Vision and Opthalmology},
  address   = {Rockville},
  issn      = {1534-7362},
  doi       = {10.1167/17.13.3},
  pages     = {1626 -- 1638},
  year      = {2017},
  abstract  = {When watching the image of a natural scene on a computer screen, observers initially move their eyes toward the center of the image—a reliable experimental finding termed central fixation bias. This systematic tendency in eye guidance likely masks attentional selection driven by image properties and top-down cognitive processes. Here, we show that the central fixation bias can be reduced by delaying the initial saccade relative to image onset. In four scene-viewing experiments we manipulated observers' initial gaze position and delayed their first saccade by a specific time interval relative to the onset of an image. We analyzed the distance to image center over time and show that the central fixation bias of initial fixations was significantly reduced after delayed saccade onsets. We additionally show that selection of the initial saccade target strongly depended on the first saccade latency. A previously published model of saccade generation was extended with a central activation map on the initial fixation whose influence declined with increasing saccade latency. This extension was sufficient to replicate the central fixation bias from our experiments. Our results suggest that the central fixation bias is generated by default activation as a response to the sudden image onset and that this default activation pattern decreases over time. Thus, it may often be preferable to use a modified version of the scene viewing paradigm that decouples image onset from the start signal for scene exploration to explicitly reduce the central fixation bias.},
  language  = {en}
}